In vivo Evaluation of Lymph Nodes Using Quantitative Ultrasound

Project Summary This application proposes a collaborative study to be performed by Weill Cornell Medicine (WCM) in New York, NY, as the lead organization in collaboration with GE Research (GER) in Niskayuna, NY, and Stony Brook Medicine (SBM) affiliated with the State University of New York in Stony Brook, NY. The proposed

project addresses the need for reliable, highly sensitive means of detecting metastases to lymph nodes (LNs) and distinguishing them from primary lymphomas and LNs affected by benign conditions. This capability will allow improved staging and treatment of disease. Accordingly, we seek to validate existing

encouraging results obtained in prior studies by WCM and SBM using quantitative-ultrasound (QUS) methods to detect metastases in LNs by applying and evaluating these promising methods using a modified clinical scanner to acquire ultrasonic echo-signal data from patients undergoing medically required ultrasonically guided fine-needle aspirations (FNAs) of suspicious LNs. The study will include a

far broader range of disease types from a larger and more-diverse patient population than has been possible in studies to date. Validation efforts will develop, deploy, and validate an application for QUS- based detection and characterization of LNs implemented in a clinical scanner. Successful validation will

establish a foundation for incorporation of QUS-based methods for LN evaluation into clinical ultrasonic scanners. Such instruments will be able to evaluate suspicious LNs intra-operatively in the operating room or pre-operatively in the examination room. The collaborating researchers will synergize capabilities in the

following general ways: WCM will provide project oversight and coordination and will apply, refine, and test QUS and classification methods; GER will provide ultrasound instrumentation and will develop and validate the QUS application for the scanner; SBM will recruit patients and acquire ultrasonic and

cytopathological data from patients undergoing FNAs of their LNs.